通过学习人造形状生成器实现联合形状分析，支持精准匹配与分割。
GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations
- 设计仿射正则化损失，使隐空间相近的生成形状保持近似仿射变形。
- 在ShapeNet上实现优于以往方法的形状匹配与联合分割性能。
- 适合需要高精度形状分析与结构保真的几何建模研究者使用。
我们提出GenAnalysis，一种隐式形状生成框架，用于人造形状的联合分析，包括形状匹配与联合分割。核心思想是强制隐式生成器中潜空间相近的合成形状之间保持尽可能仿射的变形，通过设计正则化损失实现。该方法可理解每个形状在邻近形状上下文中的变化，并支持结构保持的形状插值。我们通过在每种形状切空间中恢复分段仿射向量场来提取这些形状变化，该向量场提供单个形状的分割线索。随后通过迭代传播仿射变形，在一系列中间形状间建立形状对应关系，并将单形状分割线索聚合为一致的联合分割结果。在ShapeNet数据集上的实验表明，该方法在形状匹配与联合分割任务上均优于先前方法。
原文摘要 · Abstract (English)
We present GenAnalysis, an implicit shape generation framework that allows joint analysis of man-made shapes, including shape matching and joint shape segmentation. The key idea is to enforce an as-affine-as-possible (AAAP) deformation between synthetic shapes of the implicit generator that are close to each other in the latent space, which we achieve by designing a regularization loss. It allows us to understand the shape variation of each shape in the context of neighboring shapes and also offers structure-preserving interpolations between the input shapes. We show how to extract these shape variations by recovering piecewise affine vector fields in the tangent space of each shape. These vector fields provide single-shape segmentation cues. We then derive shape correspondences by iteratively propagating AAAP deformations across a sequence of intermediate shapes. These correspondences are then used to aggregate single-shape segmentation cues into consistent segmentations. We conduct experiments on the ShapeNet dataset to show superior performance in shape matching and joint shape segmentation over previous methods.
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